Multivariate, predictive regulation of a direct reduction process

ABSTRACT

For the prognosis of a value of a characteristic of a product which is to be produced with the aid of a neuronal network, the history of the product is taken into account when determining an input variable of an input neuron of the neuronal network.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is based on and hereby claims priority to GermanApplication No. 10306024.3 filed on Feb. 13, 2003, the contents of whichare hereby incorporated by reference.

BACKGROUND OF THE INVENTION

The direct reduction of iron ore to the product sponge iron in the formof DRI (Direct Reduced Iron) or HBI (Hot Briquetted Iron) is undertakenusing heated process gases, preferably in a shaft furnace. In simpleterms this involves chemical reactions which convert iron ore (Fe₂O₃)and natural gas (CH₄) into iron Fe, carbon dioxide CO₂ and water H₂O.

The shaft furnace is in continuous operation in that raw material isconstantly fed into it from above in the form of iron ore pellets andthe sponge iron is withdrawn at the bottom in a similar continuousprocess.

Methods of producing sponge iron are for example known from U.S. Pat.No. 4,093,455, U.S. Pat. No. 4,178,151, U.S. Pat. No. 4,234,169 and WO02/097138 A1. Further publications dealing with this subject are:Thompson, M.: “Control Innovations in MIDREX Plants: An Introduction” in“Direct from MIDREX”, 1st Quarter 2001, P. 3-4, 2001 and Gömer, F.,Bacon, F.: “Development of Process Automation for the MIDREX Process” in“Direct from MIDREX”, 1st Quarter 2002, P. 3-5, 2002.

In the production of sponge iron it is desirable to produce a productwith properties which are as constant as possible and preciselyspecified. To this end it is known that all factors which mightinfluence the product to be produced are to be kept as constant aspossible and thereby the process is to be operated at a known workingpoint. However even the assumption of a fully homogeneous iron ore asraw material for example is often not fulfilled.

SUMMARY OF THE INVENTION

Using this as its starting point, an underlying object of the inventionis to predict a property of a product to be produced, especially spongeiron produced by direct reduction.

The general idea underlying the inventions is, in the prognosis of avalue of a property to be predicted with the aid of a neuronal network,to take account of the product history by entering values into theprognosis which the property to be predicted exhibits in productsalready produced. Such values can be taken into account for thedetermination of an input variable of the neuronal network. As analternative or in addition it is however also possible, when determiningthe same input variable or another input variable of the neuronalnetwork, to take account of the difference between a value of theproperty measured at a specific point in time and the value of theproperty predicted for this point in time.

Accordingly in a method, especially a production method, a prognosis fora value of a property, especially one that can only be measured in thefuture, of a product to be manufactured in the present or in the future,is undertaken with the aid of a neuronal network. In this case a valueof the property is measured, be it complete or with the aid of spotchecks, at different points in time of products produced in the past.Then, from the values of the property measured for the points in time inthe past, the value of the property of the product to be manufactured inthe present and/or future is predicted in a preliminary prognosis. Thisinvolves a prognosis into the unclear so to speak, which only includesthe values of the property measured for the times in the past.

This value predicted in the preliminary prognosis is taken into accountin determining an input variable for an input neuron of the neuronalnetwork. With the aid of the neuronal network, the value of the propertyof the product to be produced in the present and/or the future isfinally predicted in the actual prognosis.

The preliminary prognosis is preferably undertaken with the aid of arecursive filter. The recursive filter can be implemented relativelysimply by performing a linear projection. More precise prognoses arepossible by using a second neuronal network as a recursive filter,especially a recurrent neuronal network For this an additionalmathematical description of the cause-effect relationship betweenprocess parameters and the property is worthwhile.

For determining an input variable of the neuronal network a comparisonbetween a value of the property measured for a point in time in the pastand a predicted value of the property for this point in time can howeveralso be taken into account for determining an input variable for theneuronal network.

Accordingly a prognosis of a value of a property which can especiallyonly be measured in the future of a product to be produced in thepresent or in the future is undertaken with the aid of a neuronalnetwork. To this end a value of the property is measured for a productproduced at a specific time. Furthermore a value of the property for theproduct produced at this time is predicted with the prognosis. To thisend the prognosis for the past must be initialized with random ormeaningfully selected values from a point even further back in the past.Then the difference between the measured and the predicted value of theproperty of the product at this point is formed. This difference isincluded in the determination of an input variable for an input neuronof the neuronal network. With the aid of the neuronal network finallythe value of the property of the product to be produced in the presentand/or the future is predicted in the prognosis.

The aim of a good prognosis is to intelligently control the productionprocess of a product. In the method or through the method parameters arepreferably therefore modified in the production process of the productto be produced until the value of the property of the product to beproduced predicted in the prognosis at least approximately matches arequired value of the property of the product to be produced.

Although the method described is generally suitable for all possibleproducts, it is however especially relevant to those products for whichthe properties cannot already be measured during their production orshortly thereafter, but only with a delay of under some circumstancesseveral hours. The method may be used especially advantageously forcontinuous production processes.

The method of prognosis described is especially suitable for theprognosis of the properties of sponge iron produced in a directreduction process. Accordingly the property can consist of one or moreof the variables given below:

-   -   The degree of metallization, that is the relationship between        the absolute iron content in the iron ore and the released iron        (Fe),    -   the proportion by weight of the sponge iron which is present as        metallic iron (Fe),    -   the carbon content in the sponge iron.

Naturally it falls within the scope of the invention to use theprognosis method to predict not just one property but a number ofproperties of the product to be produced.

For the prognosis with the aid of the neuronal network, as well as thehistory of the products already produced, account should also be takenof further parameters in the production process of the product to beproduced, in that they influence or represent input variables of inputneurons of the neuronal network. Such parameters are in particularprocess temperatures, combinations of the process gases used and/orproperties of the raw materials.

The invention further relates to an arrangement which is set up toexecute the method given here. Such an arrangement can be implementedfor example by corresponding programming and setup of a computers or adata processing system. A direct reduction plant can also be part of thearrangement, especially with a shaft furnace.

A program product for a data processing system which contains codesections with which one of the methods described can be executed on thedata processing system, can be executed through suitable implementationof the method in a programming language and compilation into code whichcan be executed by the data processing system. The sections of code arestored for this purpose. In this case a computer program product istaken to mean the program as a marketable product. It can be availablein any form, for example on paper, on a computer-readable data medium ordistributed over a network.

Using this as its starting point, an underlying object of the inventionis to predict a property of a product to be produced, especially spongeiron produced by direct reduction.

The general idea underlying the inventions is, in the prognosis of avalue of a property to be predicted with the aid of a neuronal network,to take account of the product history by entering values into theprognosis which the property to be predicted exhibits in productsalready produced. Such values can be taken into account for thedetermination of an input variable of the neuronal network. As analternative or in addition it is however also possible, when determiningthe same input variable or another input variable of the neuronalnetwork, to take account of the difference between a value of theproperty measured at a specific point in time and the value of theproperty predicted for this point in time.

Accordingly in a method, especially a production method, a prognosis fora value of a property, especially one that can only be measured in thefuture, of a product to be manufactured in the present or in the future,is undertaken with the aid of a neuronal network. In this case a valueof the property is measured, be it complete or with the aid of spotchecks, at different points in time of products produced in the past.Then, from the values of the property measured for the points in time inthe past, the value of the property of the product to be manufactured inthe present and/or future is predicted in a preliminary prognosis. Thisinvolves a prognosis into the unclear so to speak, which only includesthe values of the property measured for the times in the past.

This value predicted in the preliminary prognosis is taken into accountin determining an input variable for an input neuron of the neuronalnetwork. With the aid of the neuronal network, the value of the propertyof the product to be produced in the present and/or the future isfinally predicted in the actual prognosis.

The preliminary prognosis is preferably undertaken with the aid of arecursive filter. The recursive filter can be implemented relativelysimply by performing a linear projection. More precise prognoses arepossible by using a second neuronal network as a recursive filter,especially a recurrent neuronal network For this an additionalmathematical description of the cause-effect relationship betweenprocess parameters and the property is worthwhile.

For determining an input variable of the neuronal network a comparisonbetween a value of the property measured for a point in time in the pastand a predicted value of the property for this point in time can howeveralso be taken into account for determining an input variable for theneuronal network.

Accordingly a prognosis of a value of a property which can especiallyonly be measured in the future of a product to be produced in thepresent or in the future is undertaken with the aid of a neuronalnetwork. To this end a value of the property is measured for a productproduced at a specific time. Furthermore a value of the property for theproduct produced at this time is predicted with the prognosis. To thisend the prognosis for the past must be initialized with random ormeaningfully selected values from a point even further back in the past.Then the difference between the measured and the predicted value of theproperty of the product at this point is formed. This difference isincluded in the determination of an input variable for an input neuronof the neuronal network. With the aid of the neuronal network finallythe value of the property of the product to be produced in the presentand/or the future is predicted in the prognosis.

The aim of a good prognosis is to intelligently control the productionprocess of a product. In the method or through the method parameters arepreferably therefore modified in the production process of the productto be produced until the value of the property of the product to beproduced predicted in the prognosis at least approximately matches arequired value of the property of the product to be produced.

Although the method described is generally suitable for all possibleproducts, it is however especially relevant to those products for whichthe properties cannot already be measured during their production orshortly thereafter, but only with a delay of under some circumstancesseveral hours. The method may be used especially advantageously forcontinuous production processes.

The method of prognosis described is especially suitable for theprognosis of the properties of sponge iron produced in a directreduction process. Accordingly the property can consist of one or moreof the variables given below:

-   -   The degree of metallization, that is the relationship between        the absolute iron content in the iron ore and the released iron        (Fe),    -   the proportion by weight of the sponge iron which is present as        metallic iron (Fe),    -   the carbon content in the sponge iron.

Naturally it falls within the scope of the invention to use theprognosis method to predict not just one property but a number ofproperties of the product to be produced.

For the prognosis with the aid of the neuronal network, as well as thehistory of the products already produced, account should also be takenof further parameters in the production process of the product to beproduced, in that they influence or represent input variables of inputneurons of the neuronal network. Such parameters are in particularprocess temperatures, combinations of the process gases used and/orproperties of the raw materials.

The invention further relates to an arrangement which is set up toexecute the method given here. Such an arrangement can be implementedfor example by corresponding programming and setup of a computers or adata processing system. A direct reduction plant can also be part of thearrangement, especially with a shaft furnace.

A program product for a data processing system which contains codesections with which one of the methods described can be executed on thedata processing system, can be executed through suitable implementationof the method in a programming language and compilation into code whichcan be executed by the data processing system. The sections of code arestored for this purpose. In this case a computer program product istaken to mean the program as a marketable product. It can be availablein any form, for example on paper, on a computer-readable data medium ordistributed over a network.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects and advantages of the present invention willbecome more apparent and more readily appreciated from the followingdescription of an exemplary embodiment, taken in conjunction with theaccompanying drawings of which:

FIG. 1 is a cross section of a shaft furnace for producing sponge iron;

FIG. 2 is a graph of a preliminary prognosis of a value of a property ofa product to be produced based on values of the properties of productsalready produced.

FIG. 3 is a graph of a preliminary prognosis of a value of a product tobe produced based on a measured value of the property for a productalready produced and on a predicted value of the property for thisproduct already produced.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Reference will now be made in detail to the preferred embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings, wherein like reference numerals refer to like elementsthroughout.

FIG. 1 shows a shaft furnace 1 with a feed 2 for feeding in the iron orepellets which pile up into one or more piles 3 within the shaft furnace1. The shaft furnace 1 further features components 4 for feeding inreaction gases, especially oxygen and carbon monoxide, and components 5drawing off reaction gases, especially carbon dioxide and water.

Inside the shaft furnace 1 the iron ore is reduced at high temperatureand under the effects of the process gases in a direct reduction processto sponge iron, which is removed at the lower end of the furnace via aremoval device 6 and taken away on a conveyor belt 7.

At regular intervals, for example every two to four hours, samples ofcooled products are taken and investigated in the laboratory for theproperties of metallization or carbon content. The time difference fromthe conclusion of the production process to the evaluated sample isaround 5 to 9 hours for the metallization and around 3.5 to 7.5 hoursfor enrichment with carbon. The reason for this is that metallization iscompleted as a subprocess, approximately in the center of the shaftfurnace 1. The product produced however remains in the shaft furnace 1for some further time, around 3 hours, to cool off, before being letthough to the removal device 6 to leave the furnace. The subprocessenrichment with carbon is completed in around three quarters of thetime, i.e. after around 4.5 hours. In addition around 2 hours are neededin the laboratory for the measurement of the metallization value and themeasurement of the carbon enrichment value. Under conditions where themeasurement is taken every 4 hours, this produces a dead time of 5 to 9or 3.5 to 7.5 hours respectively.

Under the condition that all influencing factors remain constant, thisallows regular interventions to be made into the ongoing process. Forthe preceding 3.5 to 9 hours of the process however there is no furtheropportunity to intervene. In practice it is additionally not possible tokeep all influencing factors constant.

FIG. 2 shows that the value W-₃ of a property, that is for example ofthe metallization or of the carbon content of the product, present atthe point t-₃, cannot be measured until the point t-₃+5 hours. The sameapplies in a similar fashion for the value W-₂ of the property of theproduct at point t-₂ and the value W-₁ of the product at the point t-₁,which can also not be measured until 5 hours after production.

From the measured values W-₃, W-₂ and W-₁, which for example specify ametallization of 94.1%, 94.2% and 94 3%, the value W^(V) _(p,0) of theproperty to be produced at the current time t₀ is now predicted in apreliminary prognosis. In the exemplary embodiment shown in FIG. 2 thisis done simply by linear prediction, which is indicated by the dashedline. However more complicated algorithms can also be used here.

The value W^(V) ₀ predicted in the preliminary prognosis is now takeninto account for the determination of an input variable for an inputneuron of a neuronal network.

In addition, as shown in FIG. 3, the difference between a value W-₁measured at a point t-₁ in the past and a value W_(p,-1) predicted forthis point in time with the actual prognosis P is taken into account ina second preliminary prognosis V′.

This is done using the following formula:W ^(V″) ₀ =W ₋₁+α·(W ^(P) ₋₁ −W ₋₁);With α={0; 1}, for example α=0.25.

This value W^(V″) 0 of a second prognosis formed taking into account thedifference between the measured value W₋₁ and the value W^(P) ₋₁ of theproperty of the product at time t₋₁ predicted by the prognosis, is nowused as the input variable for a second input neuron of the neuronalnetwork.

The input variables of further input neurons of the neuronal network areformed by further process parameters or calculated taking into accountfurther process parameters. Such process parameters are:

-   -   The composition of all process gases (dry and wet), used in the        direct reduction plant,    -   Information about quantities and times for gas throughflows (for        example tons per hours) and temperature,    -   All temperature measurements in and at the shaft furnace,    -   Properties describing the raw material (porosity, chemical        composition, size and form of the pellets, density,        temperature),    -   Properties describing the product produced or to be produced        (density, chemical composition, carbon content, iron content,        metallic iron content, degree of metallization),    -   Mass flows of the raw material and of the end product, and of    -   Air (temperature and humidity over time).

Advantageously around 100 measured and computed input variables areincluded in the model calculation.

With the neuronal network It has proved especially advantageous to usean ensemble of neuronal networks and to evaluate their median. Acombination of Feed-Forward networks with recursive filters is alsoadvantageous.

Some of the preferred training methods for the neuronal network are asfollows: The use of digital filters for the training data, an automaticexception removal, bagging, adherence to peripheral conditions for themonotony as regards relevant control variables and target variables.

The input variables of the neuronal network are preferably analyticallymodelled from the given process parameters. This is done for example bythe integration of variables, the quantitative description of chemicalconversion including the reaction kinetics with the aid of differentialequations and a calculation as to when which item of material is wherein the shaft furnace, with the aid of a product yield per hour.

For execution different, linked software programs in Fortran, C, C++ andMATLAB are advantageously used. A user interface can be programmed inVisual-Basic.

The invention provides the following advantages:

-   -   The deviation of predetermined required values can be reduced.        In practice a reduction of the standard deviation by appr. 40%        for metallization and appr. 30% for carbon content is produced.    -   Lower deviations allow an operation of the shaft furnace which        is closer to the optimum, which benefits throughput and quality.    -   A production increase of appr. 1% is achieved.    -   Through the more constant and higher material quality the        consumers of the sponge iron, namely network operators of        electric arc furnaces, can operate their furnaces more        efficiently. This advantage is even greater than the advantage        for operation of the direct reduction plant, so that        correspondingly higher prices can be obtained for the product        produced.    -   Computation of the material properties can save on laboratory        samples.    -   The computation can be undertaken at any time. This means that        current values can be computed every 0.1 seconds for example,        instead of being analyzed in the laboratory every two to four        hours.    -   Since definitive chemical reactions are completed within the        first half or the first third of the process of appr. 6 hours,        the material properties can already be determined before the        sponge iron is removed from the shaft furnace. This reduces the        reaction times for regulation of the material properties from        3.5 to 9 hours to the calculation time for the model, which lies        below 0.1 seconds.

The invention has been described in detail with particular reference topreferred embodiments thereof and examples, but it will be understoodthat variations and modifications can be effected within the spirit andscope of the invention covered by the claims which may include thephrase “at least one of A, B and C” as an alternative expression thatmeans one or more of A, B and C may be used, contrary to the holding inSuperguide v. DIRECTV, 69 USPQ2d 1865 (Fed. Cir. 2004).

1-13. (canceled)
 14. A production method for predicting, using a firstneuronal network, a value of a property of a product to be produced,comprising: subjecting a production process of the product to anon-linear dynamic; measuring values of the property of the product asproduced at different times; predicting in a preliminary prognosis apreliminary value of the property of the product to be produced;determining an input variable for an input neuron of the first neuronalnetwork, taking into account the preliminary value; and predicting thevalue of the property of the product to be produced in a subsequentprognosis using the first neuronal network, where the first neuronalnetwork includes a non-linear part.
 15. A method in accordance withclaim 14, wherein said predicting in the preliminary prognosis utilizesa recursive filter.
 16. A method in accordance with claim 15, whereinthe recursive filter includes a linear projection.
 17. A method inaccordance with claim 16, wherein the recursive filter includes asecond, recurrent neuronal network
 18. A method in accordance with claim17, further comprising measuring a reference value of the property ofthe product, wherein said predicting in the preliminary prognosisobtains an estimated value of the property of the product, and whereinsaid determining includes calculating a difference between the referencevalue and the estimated value of the property of the product, used todetermine the input variable for the input neuron of the neuronalnetwork.
 19. A method in accordance with claim 18, further comprisingchanging parameters in the production process of the product, until thevalue of the property of the product predicted in the subsequentprognosis substantially corresponds to a required value of the propertyof the product.
 20. A method in accordance with claim 19, wherein theproduction process of the product includes direct reduction.
 21. Amethod in accordance with claim 20, wherein the product contains ironsponge.
 22. A method in accordance with claim 21, wherein the propertyof the product is metallic iron content.
 23. A method in accordance withclaim 22, wherein the property of the product is carbon content.
 24. Amethod in accordance with claim 23, wherein said subsequent prognosisusing the first neuronal network takes into account parameters in theproduction process of the product, including at least one of processtemperatures, compositions of process gases used, properties of rawmaterials and parameters determined through analytical computations ofchemical conversions and reaction kinetics.
 25. A program product whichwhen loaded and executed on a data processing system controls the dataprocessing system to perform a method in accordance with one of claims14 to
 24. 26. A system for predicting a value of a property of a productto be produced, comprising: means for subjecting a production process ofthe product to a non-linear dynamic; means for measuring values of theproperty of the product as produced at different times; means forpredicting in a preliminary prognosis a preliminary value of theproperty of the product to be produced; means for determining an inputvariable taking into account the preliminary value; and a neuronalnetwork, having a non-linear part and an input neuron receiving theinput variable, predicting the value of the property of the product tobe produced in a subsequent prognosis.